[Typing] 修改一些示例代码中的类型错误 part 3 (#65545)

megemini · web-flow · commit 0130fbab873a · 2024-07-01T10:21:20.000+08:00
diff --git a/python/paddle/base/framework.py b/python/paddle/base/framework.py
@@ -1806,8 +1806,8 @@ def numpy(self):
                 >>> data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
                 >>> with base.dygraph.guard():
                 ...     linear = Linear(32, 64)
-                ...     data = paddle.to_tensor(data)
-                ...     x = linear(data)
+                ...     data_tensor = paddle.to_tensor(data)
+                ...     x = linear(data_tensor)
                 ...     print(x.numpy())
 
         """
@@ -1837,7 +1837,7 @@ def backward(self, retain_graph=False):
                 >>> import paddle
                 >>> paddle.disable_static()
 
-                >>> x = np.ones([2, 2], np.float32)
+                >>> x = np.ones([2, 2], np.float32) # type: ignore[var-annotated]
                 >>> inputs = []
                 >>> for _ in range(10):
                 ...     tmp = paddle.to_tensor(x)
@@ -1881,7 +1881,7 @@ def gradient(self):
                 >>> import numpy as np
 
                 >>> # example1: return ndarray
-                >>> x = np.ones([2, 2], np.float32)
+                >>> x = np.ones([2, 2], np.float32) # type: ignore[var-annotated]
                 >>> with base.dygraph.guard():
                 ...     inputs2 = []
                 ...     for _ in range(10):
@@ -1903,8 +1903,8 @@ def gradient(self):
                 ...         sparse=True)
                 ...     x_data = np.arange(12).reshape(4, 3).astype('int64')
                 ...     x_data = x_data.reshape((-1, 3, 1))
-                ...     x = paddle.to_tensor(x_data)
-                ...     out = embedding(x)
+                ...     x_tensor = paddle.to_tensor(x_data)
+                ...     out = embedding(x_tensor)
                 ...     out.backward()
                 ...     print(embedding.weight.gradient())
 
@@ -1930,7 +1930,7 @@ def clear_gradient(self):
                 >>> import paddle.base as base
                 >>> import numpy as np
 
-                >>> x = np.ones([2, 2], np.float32)
+                >>> x = np.ones([2, 2], np.float32) # type: ignore[var-annotated]
                 >>> inputs2 = []
                 >>> for _ in range(10):
                 >>>     tmp = paddle.to_tensor(x)
diff --git a/python/paddle/onnx/export.py b/python/paddle/onnx/export.py
@@ -83,7 +83,7 @@ def export(layer, path, input_spec=None, opset_version=9, **configs):
             ...     # Static and run model.
             ...     paddle.jit.to_static(model)
             ...     out = model(x, y, z=True)
-            ...     paddle.onnx.export(model, 'pruned', input_spec=[x, y, z], output_spec=[out], input_names_after_prune=[x])
+            ...     paddle.onnx.export(model, 'pruned', input_spec=[x, y, True], output_spec=[out], input_names_after_prune=[x])
             ...
             >>> export_logic()
     """
diff --git a/python/paddle/static/nn/common.py b/python/paddle/static/nn/common.py
@@ -4111,23 +4111,23 @@ class ExponentialMovingAverage:
 
             >>> for pass_id in range(3):
             ...     for batch_id in range(6):
-            ...         data = numpy.random.random(size=(10, 5)).astype('float32')
+            ...         feed_data = numpy.random.random(size=(10, 5)).astype('float32')
             ...         exe.run(program=static.default_main_program(),
-            ...         feed={'x': data},
+            ...         feed={'x': feed_data},
             ...         fetch_list=[cost.name])
 
             ...     # usage 1
             ...     with ema.apply(exe):
-            ...         data = numpy.random.random(size=(10, 5)).astype('float32')
+            ...         feed_data = numpy.random.random(size=(10, 5)).astype('float32')
             ...         exe.run(program=test_program,
-            ...             feed={'x': data},
+            ...             feed={'x': feed_data},
             ...             fetch_list=[hidden.name])
 
             ...     # usage 2
             ...     with ema.apply(exe, need_restore=False):
-            ...         data = numpy.random.random(size=(10, 5)).astype('float32')
+            ...         feed_data = numpy.random.random(size=(10, 5)).astype('float32')
             ...         exe.run(program=test_program,
-            ...             feed={'x': data},
+            ...             feed={'x': feed_data},
             ...             fetch_list=[hidden.name])
             ...     ema.restore(exe)
 
diff --git a/python/paddle/vision/models/vgg.py b/python/paddle/vision/models/vgg.py
@@ -71,7 +71,7 @@ class VGG(nn.Layer):
 
             >>> vgg11_cfg = [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']
 
-            >>> features = make_layers(vgg11_cfg)
+            >>> features = make_layers(vgg11_cfg)  # type: ignore
 
             >>> vgg11 = VGG(features)
 
diff --git a/python/paddle/vision/transforms/transforms.py b/python/paddle/vision/transforms/transforms.py
@@ -232,7 +232,7 @@ class BaseTransform(_Transform[_InputT, _RetT]):
             ...     else:
             ...         raise TypeError("Unexpected type {}".format(type(img)))
             ...
-            >>> class CustomRandomFlip(BaseTransform):
+            >>> class CustomRandomFlip(BaseTransform): # type: ignore
             ...     def __init__(self, prob=0.5, keys=None):
             ...         super().__init__(keys)
             ...         self.prob = prob
diff --git a/test/dygraph_to_static/simnet_dygraph_model.py b/test/dygraph_to_static/simnet_dygraph_model.py
@@ -299,15 +299,15 @@ class FC(paddle.nn.Layer):
     Examples:
         .. code-block:: python
 
-            import paddle
-            import paddle.base as base
-            from paddle.base.dygraph import FC
-            import numpy as np
-            data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
-            with base.dygraph.guard():
-                fc = FC("fc", 64, num_flatten_dims=2)
-                data = paddle.to_tensor(data)
-                conv = fc(data)
+            >>> import paddle
+            >>> import paddle.base as base
+            >>> from paddle.base.dygraph import FC
+            >>> import numpy as np
+            >>> data = np.random.uniform(-1, 1, [30, 10, 32]).astype('float32')
+            >>> with base.dygraph.guard():
+            ...     fc = FC("fc", 64, num_flatten_dims=2)
+            ...     data_tensor = paddle.to_tensor(data)
+            ...     conv = fc(data_tensor)
     """
 
     def __init__(
